class_docstr: Flavor backend implementation for the generic R models.
  Predict and serve locally models with 'crate' flavor.
class_docstr_tok: ["Flavor","backend","implementation","for","the","generic","R","models",".","Predict","and","serve","locally","models","with","'crate","'","flavor","."]
docstr: Generate predictions using R model saved with MLflow.
  Return the prediction results as a JSON.
docstr_tok: ["Generate","predictions","using","R","model","saved","with","MLflow",".","Return","the","prediction","results","as","a","JSON","."]
code: def predict(
      self,
      model_uri,
      input_path,
      output_path,
      content_type,
      pip_requirements_override=None,
  ):
      """
      Generate predictions using R model saved with MLflow.
      Return the prediction results as a JSON.
      """
      if pip_requirements_override is not None:
          raise MlflowException(
              "pip_requirements_override is not supported in the R backend."
          )
      model_path = _download_artifact_from_uri(model_uri)
      str_cmd = (
          "mlflow:::mlflow_rfunc_predict(model_path = '{0}', input_path = {1}, "
          "output_path = {2}, content_type = {3})"
      )
      command = str_cmd.format(
          quote(model_path),
          _str_optional(input_path),
          _str_optional(output_path),
          _str_optional(content_type),
      )
      _execute(command)
  
file_path: mlflow/mlflow/rfunc/backend.py
imports: import logging
  import os
  import re
  import subprocess
  import sys
  from mlflow.exceptions import MlflowException
  from mlflow.models import FlavorBackend
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.string_utils import quote
label_desc: Use image node_id 3 for calling the RFuncBackend obj's underlying member method code with example usage: obj.predict(model_uri, input_path, output_path, content_type, pip_requirements_override) without return types